System and method of predicting community member responsiveness

ABSTRACT

Disclosed herein is a mechanism, which may be in a community-driven system, to assist a user, or member of the community, to articulate a request, such as a question, that is likely to receive a response, e.g., an answer to a question posed, from the user community. A machine learning approach may be used to generate a model, which model may be trained using previously-posted requests, e.g., questions, and response, e.g., answers. The generated model may be used to make a prediction, e.g., to predict a potential number of answers for a given question, which prediction may also have an accompanying confidence score. A prediction generated by the model may be presented to the user as the user is providing the input, e.g., typing a question. Such prediction may be generated for a partial question, e.g., as the question is being typed, as well as a completed question.

FIELD OF THE DISCLOSURE

The present disclosure relates to a community-driven system in which auser may request a response from other community members as users of thesystem, such a request may be in the form of a question to elicit aresponse from one or more other users in the form of an answer, and moreparticularly, to predicting a likelihood that a user's request willreceive a response from one or more users.

BACKGROUND

A community-driven system typically has a community of users, and anyuser may ask other users in the community for a response. By way of oneexample, Yahoo! Answers is a community-driven question and answersystem, which allows a user to ask a question and for a user to answer aquestion posed by a user. To illustrate further, a user may post aquestion, which is available for review by any user of the system. If auser wishes to do so, a user may submit a response, or answer, to aposted question using the system. The system can be used to viewquestions as well as answers to the questions.

SUMMARY

It is beneficial in the community-driven system, such as Yahoo answers,to attract as many users to the system. The quality of questions askedand answers provided are factors in attracting and maintaining acommunity of users. The present disclosure seeks to address this andother considerations and to address failings in the art and provide asystem and method of predicting the likelihood of receiving a responseto a given request for a response.

By way of a non-limiting example, where a community driven systemelicits questions and answers to questions from its community of users,embodiments of the present disclosure predict a likelihood that aquestion posted by a user from the community is answered by one or moreusers of the community. To further illustrate, the prediction may beprovided to the user that is asking the question so that the user canform a question that is likely to receive a response, or responses, fromthe user community. While embodiments of the present disclosure aredescribed in connection with a question and answer system, it should beapparent that embodiments of the present disclosure may be used with anycommunity-driven system to predict a likelihood of a community'sresponse to received input, e.g., a request for a response, from amember of the community.

Embodiments of the present disclosure provide a mechanism that may beused in a community-driven application or system to assist a user, ormember of the community, to articulate a request, such as a question,that is likely to receive a response, e.g., an answer to a questionposed, from the user community. In accordance with one or more suchembodiments, a machine learning approach may be used to generate a modelusing previously-posted questions and answers, which model may be usedwith a present question to make a prediction, e.g., to predict apotential number of answers for the present question, which predictionmay also have an accompanying confidence score. In accordance with oneor more embodiments, the prediction generated by the model may bepresented to the user as the user is providing the input, e.g., typing aquestion. Such prediction may be generated for a partial question, e.g.,as the question is being typed, as well as a completed question, e.g.,once the user has finished entering the question and before or aftersubmitting the question for a response.

The prediction that is provided to the user may be used by the user todetermine whether or not to submit, or post, a question for access bythe community of users, and/or modify the question to improve the chanceof a response to the question. A prediction generated in accordance withone or more embodiments can improve quality of the questions asked aswell as the answers provided in response, such that users belonging to acommunity that uses a community-driven application may be provided witha more interactive and enjoyable experience. Additionally and where auser is informed, in accordance with one or more embodiments of thepresent disclosure, that the user's question is unlikely to receive aresponse, the user is less likely to submit the question, and is morelikely to pose a modified or different question. This will result in areduction in unanswered questions, as questions that are received by thecommunity-driven application are more likely to receive a response fromthe community. Furthermore and where a user elects to submit a questionthat has a low probability of receiving a response, the user is at leastforewarned that such is a likely outcome.

In accordance with one or more embodiments, a method is provided, themethod comprising receiving, via at least one computing device, inputfrom a user member of a community of users of a community-drivenapplication, the input comprising at least a portion of a request forresponse to be directed to the community of users; determining, via theat least one computing device, a plurality of features of the request;determining, via the at least one computing device and using aprediction model, feedback comprising a prediction of a probability ofresponse by the community of users to the request; and providing, viathe at least one computing device, the feedback to the user in responseto the user's input.

Embodiments of the present disclosure further provide a systemcomprising at least one computing device comprising one or moreprocessors to execute and memory to store instructions to receive inputfrom a user member of a community of users of a community-drivenapplication, the input comprising at least a portion of a request forresponse to be directed to the community of users; determine a pluralityof features of the request; determine, using a prediction model,feedback comprising a prediction of a probability of response by thecommunity of users to the request; and provide the feedback to the userin response to the user's input.

Embodiments of the present disclosure further provide a computerreadable non-transitory storage medium for tangibly storing thereoncomputer readable instructions that when executed cause at least oneprocessor to receive input from a user member of a community of users ofa community-driven application, the input comprising at least a portionof a request for response to be directed to the community of users;determine a plurality of features of the request; determine, using aprediction model, feedback comprising a prediction of a probability ofresponse by the community of users to the request; and provide thefeedback to the user in response to the user's input.

In accordance with one or more embodiments, a system is provided thatcomprises one or more computing devices configured to providefunctionality in accordance with such embodiments. In accordance withone or more embodiments, functionality is embodied in steps of a methodperformed by at least one computing device. In accordance with one ormore embodiments, program code to implement functionality in accordancewith one or more such embodiments is embodied in, by and/or on acomputer-readable medium.

DRAWINGS

The above-mentioned features and objects of the present disclosure willbecome more apparent with reference to the following description takenin conjunction with the accompanying drawings wherein like referencenumerals denote like elements and in which:

FIG. 1 provides an example of a response prediction process flow inaccordance with one or more embodiments of the present disclosure.

FIG. 2 provides a general overview of system components for use inaccordance with one or more embodiments.

FIG. 3 provides examples of information that may be collected and usedto determine features and or attributes used by trainer to train modelto make a prediction using the model in accordance with one or moreembodiments of the present disclosure.

FIGS. 4, 5 and 6 provide examples of a request and response userinterface display in accordance with one or more embodiments of thepresent disclosure.

FIG. 7 provides a table with examples of feature families that may beextracted from in accordance with one or more embodiments.

FIG. 8 provides examples of category-token pairs in accordance with oneor more embodiments of the present disclosure.

FIG. 9 provides examples of topics in accordance with one or moreembodiments of the present disclosure.

FIG. 10 illustrates some components that can be used in connection withone or more embodiments of the present disclosure.

FIG. 11 is a detailed block diagram illustrating an internalarchitecture of a computing device in accordance with one or moreembodiments of the present disclosure.

DETAILED DESCRIPTION

Subject matter will now be described more fully hereinafter withreference to the accompanying drawings, which form a part hereof, andwhich show, by way of illustration, specific example embodiments.Subject matter may, however, be embodied in a variety of different formsand, therefore, covered or claimed subject matter is intended to beconstrued as not being limited to any example embodiments set forthherein; example embodiments are provided merely to be illustrative.Likewise, a reasonably broad scope for claimed or covered subject matteris intended. Among other things, for example, subject matter may beembodied as methods, devices, components, or systems. Accordingly,embodiments may, for example, take the form of hardware, software,firmware or any combination thereof (other than software per se). Thefollowing detailed description is, therefore, not intended to be takenin a limiting sense.

Throughout the specification and claims, terms may have nuanced meaningssuggested or implied in context beyond an explicitly stated meaning.Likewise, the phrase “in one embodiment” as used herein does notnecessarily refer to the same embodiment and the phrase “in anotherembodiment” as used herein does not necessarily refer to a differentembodiment. It is intended, for example, that claimed subject matterinclude combinations of example embodiments in whole or in part.

In general, terminology may be understood at least in part from usage incontext. For example, terms, such as “and”, “or”, or “and/or,” as usedherein may include a variety of meanings that may depend at least inpart upon the context in which such terms are used. Typically, “or” ifused to associate a list, such as A, B or C, is intended to mean A, B,and C, here used in the inclusive sense, as well as A, B or C, here usedin the exclusive sense. In addition, the term “one or more” as usedherein, depending at least in part upon context, may be used to describeany feature, structure, or characteristic in a singular sense or may beused to describe combinations of features, structures or characteristicsin a plural sense. Similarly, terms, such as “a,” “an,” or “the,” again,may be understood to convey a singular usage or to convey a pluralusage, depending at least in part upon context. In addition, the term“based on” may be understood as not necessarily intended to convey anexclusive set of factors and may, instead, allow for existence ofadditional factors not necessarily expressly described, again, dependingat least in part on context.

The detailed description provided herein is not intended as an extensiveor detailed discussion of known concepts, and as such, details that areknown generally to those of ordinary skill in the relevant art may havebeen omitted or may be handled in summary fashion.

In general, the present disclosure includes a community responsivenessprediction system, method and architecture. Certain embodiments of thepresent disclosure will now be discussed with reference to theaforementioned figures, wherein like reference numerals refer to likecomponents.

In accordance with one or more embodiments, one or more features orattributes associated with a user request, e.g., a question, are used todetermine a likelihood that one or more user members of a community willrespond, e.g., provide an answer, to the request. In accordance with oneor more such embodiments, the one or more features or attributes maycomprise attributes extracted from or otherwise associated with aquestion. In accordance with one or more such embodiments, values of theattributes may be provided to a model to determine a likelihood of aresponse to the user's request.

FIG. 1 provides an example of a response prediction process flow inaccordance with one or more embodiments of the present disclosure. Theprocess flow may be implemented by any computing device or system,including without limitation a server computer that may provide acommunity-driven application or system or may be in communication with acomputing system that provides a community-driven application or system,or a client computing device, such as without limitation a clientcomputing device executing Javascript™ in a browser application. At step102, at least a portion of a question is received from a user. Thequestion may be input via a web browser application executing on theuser's computing device, which browser application is in communicationwith a community-driven frontend, which may comprise a user interface,e.g., one or more web pages that may be provided via a community-drivenapplication or system.

At step 104, information is collected for use in making a prediction. Inaccordance with one or more embodiments, the information may comprise anumber of attributes associated with the question, the user, etc. Atstep 106, a prediction, which may comprise one or more predictionmeasures, is generated using the information collected in step 104. Inaccordance with one or more embodiments, the prediction comprises ameasure of a likelihood that the request, e.g., question, will receive aresponse, e.g., an answer to a question, from one or more user membersof a community of the community-driven system. At step 108, feedback isprovided to the user that is making the request. In accordance with oneor more embodiments, the feedback may be displayed to the user, whichfeedback may comprise one or more prediction measures of the likelihoodthat user members of the community will respond to the request forinput. By way of a non-limiting example, the feedback may be displayedto the user so that the user is able to determine whether or not tomodify or supplement the request, submit the request as is, delete therequest and start a new request, etc.

In accordance with one or more embodiments, a supervised learningmethodology may be used to predict an expected or estimated number ofanswers for a given question, or portion of a question. A statisticalmodel may be trained using a set of past questions together with thenumber of answers received in response to the set of questions, suchthat the trained model is capable of estimating a probability ofreceiving an answer for a question, an expected number of answers forthe question as well as evaluating the uncertainty in this estimate,e.g., generating a confidence level for an estimate. In accordance withone or more embodiments, a statistical model may be trained in a mannerthat reflects a temporal order of the questions used as training data,such that a generated model may put more emphasis on recent data, asopposed to older data, in making a prediction. In accordance with one ormore embodiments, the attributes or features may be a diverse set, whichfeatures may be extracted from question metadata, question content, userdata, etc.

As discussed above, feedback, e.g., number of answers, may beimmediately provided to the user as the user is providing the input,e.g., as the text of the question is being typed. This allows the userto rephrase the question before actually submitting, or posting, thequestion to the system for access by the system's community of users,for example.

FIG. 2 provides a general overview of system components for use inaccordance with one or more embodiments. A trainer 214, which comprisesa feature extractor/generator 216 and a model generator 218, may be usedto generate a model definition 220. The model definition 220 generatedby model generator 218 may be used by prediction engine, or predictiongenerator, 206 to make a prediction 226, which can be forwarded tosystem 202. As discussed above, the prediction 226 may comprise one ormore measures of the likelihood that a response, or responses, 224 willbe provided to a request 222. System 202, which may provide acommunity-driven application such as a question and answer application,receives request 222 as it is being input by a user. In accordance withone or more embodiments, system 202 may receive the request 222 prior tothe user posting the request 222 for publication and access by otherusers. Thus, system 202 may provide feedback in the form of one or moreprediction measures to the user to allow the user an opportunity todetermine whether or not to post the request 222, change the request 222before posting, etc.

By way of a non-limiting example, as system 202 receives input, e.g.,some or all of request 222, system 202 may forward the input, request222, to predictor 208. Predictor 208 comprises featureextractor/generator 204 and prediction engine 206. In accordance withone or more embodiments, feature extractor/generator 204 may be the sameas feature extractor/generator 216. In accordance with one or more suchembodiments, trainer 214 and predictor 208 can share a featureextractor/generator, or use different instances of the featureextractor/generator.

Feature extractor/generator 204 of predictor 208 collects information,e.g., features or attributes associated with the user input, which maybe used by prediction engine 206 to make a prediction. Featureextractor/generator 204 may use information collected to determinefeatures/attributes. It should be apparent that examples of theinformation collected and features used herein are for illustrationpurposes only and that embodiments of the present disclosure are notlimited with respect to the information collected and/orfeatures/attributes on which a feature extractor/generator operates.

In accordance with one or more embodiments, a client computing device,e.g., a client computing device comprising a browser application andJavascript™ or other executable code may have functionality to performsome or all of the functionality shown in FIG. 1. In accordance with oneor more such embodiments, the client computing device may generateprediction 226 using model definition 220, which model definition may bereceived and stored locally by the client computing device. Inaccordance with one or more embodiments, the client computing device mayretrieve model definition 220, implement predictor 208 to obtainprediction 226, and provide feedback using prediction 226.

FIG. 3 provides examples of information that may be collected and usedto determine features and or attributes used by trainer 214 to trainmodel 220 and by predictor 208 to make a prediction using model 220. Inaccordance with one or more embodiments, feature extractor/generator 216may collect feature/attribute information from past requests andassociated responses, e.g., questions and answers received by system 202and collected over a period of time, e.g. a number of weeks. Log(s) 210may store such data, which may in turn be provided to trainer 214. Datastore, or database, 212 may comprise information about the usercommunity, e.g., information associated with requesters and respondersas members of the community serviced by system 202. In accordance withone or more embodiments, trainer 214 generates model 220 using a numberof feature/attribute values determined using data retrieved from log(s)210 and data store 212.

In the example shown in FIG. 3, information that may be used todetermine feature/attribute values include information such as acategory information, e.g., a category or hierarchy of categoriesassociated with a request, timing of a request, words used in the bodyof a request, words used in a title associated with a request, contentof a request, such as a location, subject, etc. mentioned in therequest, length of a request and/or title of a request, uniform resourcelocator(s) (URL(s)) used in a request's body and/or title, determinedsentiment, which may be determined from the content of a request's bodyand/or title, user information, such as the requesting user's identity,number of requests and/or response made by a requesting or respondinguser, number of responses made to a request, an average or mean numberof responses provided to the requesting user's previous requests, etc.Category and/or category hierarchy may comprise a topic or subjectcategory or category hierarchy, for example. By way of a furthernon-limiting example, time of a request may comprise time of day, day ofthe week, week of the year, year, etc. Content of a request and/or titlemay be analyzed to answer interrogative inquiries, e.g., to identifyperson(s), place(s), thing(s), referenced by a request and/or answer. Inaccordance with one or more embodiments, linear discriminant analysis(LDA) may be used to determine a linear combination of features. Ofcourse it should be apparent that other types of statistical analysismay also be used with one or more embodiments of the present disclosure.Further discussion of these and other features is provided herein.

The features 228 output by the feature extractor/generator, e.g.generator 204 and/or 216, can comprise any of a number, or set, offeatures, or feature information. Features 228 may be generated fromtraining data retrieved from log(s) 210 and data store 212 and used tobuild model definition, or model, 220. Features 228 associated withrequest 222 and output by feature extractor/generator 204 may be usedwith model 220 to make a prediction of the likelihood that the request222 will receive one or more responses 224.

In accordance with one or more embodiments, a prediction 226, whichcomprises one or more measures indicating a likelihood that a requestwill receive at least one response, is generated by predictor 208 andprovided to system 202. System 202 may provide feedback to therequester, or requesting user, which feedback may include any of the oneor more measures. The feedback may be used by the requester to determinewhether or not to post the request 222, modify the request 222 beforeposting, abandon the request 222, input a new request 222, etc.

FIGS. 4, 5 and 6 provide examples of a request and response userinterface display in accordance with one or more embodiments of thepresent disclosure. In the example, the request is input in the form ofa question in body 404 and a title may be specified for the question infield 402 of user interface display 400. Display 400 further comprisesan area, such as area 416, or areas, to display feedback for review bythe user, e.g., the user entering a question in question body field 404and/or a question title in question title field 402.

In the example shown in FIGS. 4, 5 and 6, the feedback, e.g.,answerability estimation, comprises an indicator of a metric of theprobability, or likelihood, that the user will receive an answer to thequestion being posed by the user and an indicator of a metric of theestimated number of answers that the user is likely to receive inresponse to the question. In the example, the probability of an answer,or answers, is presented as a probability meter 418 having a range ofprobability values from 0 percent probability, an estimate that no onewill answer the question, to 100 percent probability, which indicatesthat the question asked by the user is most likely to receive an answeror answers.

In the example, probability meter 418 is in a form that resembles ananalog gauge with incremental markings and a pointer 412 that rotatesclockwise or counterclockwise to indicate the value or probability ofreceiving an answer. The gauge includes ranges 408 and 410, whichprovide further visual indicators of a probability or answerability ofthe user's question. Ranges 408 and 410 may be color coded. By way of anon-limiting example, range 408, which may correspond to a probabilityof less than 25% that the user will receive an answer, may red in colorto warn the user that the question as currently posed is not good forpurposes of receiving a response, as it has little likelihood ofreceiving a response. By way of a further non-limiting example, range410, which may correspond to a probability range from 25% to 50%, mightbe yellow in color to caution the user that the question has no morethan a 50% chance of receiving a response. By way of a furthernon-limiting example, although not shown, other ranges may be used, suchas a range that is colored green, which advises the user that thequestion is a good question, e.g., a question that has at least a goodif not excellent likelihood of receiving an answer. In the example,feedback area 416 includes a number 414, which provides an estimatednumber of answers that the user is likely to receive in response to thequestion.

FIG. 4 shows feedback area 416 before the user provides any input infields 402 and 404. FIG. 5 provides an example of feedback area 416 inresponse to input provided by the user in title field 402. The feedbackis displayed in display 400 as the user types the question and/orquestion title. At a point shown in FIG. 5, in response to the userinput in question title field 402, the probability meter 418 isdisplaying a probability that is just above 25% answerability, and isdisplaying indicator 414, which indicates that an estimated number ofresponses to the user's question is 29.

FIG. 6 provides another example of feedback area 416 in response to amore complete title being provided in title field 402 and a body of thequestion being provided in field 404. As can be seen, the probabilitymeter 418 indicates that the user is almost certain to receive aresponse, and indicator 414 indicates that the user is likely to receivea number, 93, of responses. It should be understood that the feedbackbeing displayed in feedback area 416 is subject to change in response touser input. Changes to the question title and or the question body willlikely change, either increase or decrease, the answerability scoreand/or the estimated number of answers.

In the example shown in FIGS. 4, 5 and 6, the user may use answerabilityfeedback provided in area 416 to determine whether or not to submit thequestion for response to the community of users. By way of anon-limiting example, had the user received the feedback shown in FIG. 5in response to the question and question title entered in FIG. 6, e.g.,an answerability just above 25%, the user might elect to modify thequestion and/or title before submitting question, or the user mightelect to abandon the question in favor of another.

In accordance with one or more embodiments, a category and/or parentcategory may be assigned to the question based on a determinedanswerability. A category and/or parent category determined for the usermay be provided as part of display 400. The user may be given anopportunity to change the category and/or parent category from the oneautomatically determined for the user. Additionally and in accordancewith one or more embodiments, certain words and/or phrases may bedetermined to reduce the likelihood that an answer, or answers, might beprovided, and/or might reduce the number of estimated answers. In such acase, where such “dampening” words or phrases are used, they may behighlighted so that the user is alerted and has the opportunity to editthe question and/or title to remove any such word or phrase. Such wordsmay vary depending on an identified, e.g., user-identified and/orsystem-identified category and/or parent category of the question. Inaccordance with one or more embodiments, typographical and/orgrammatical errors may be highlighted so that the user may correct anysuch error.

In accordance with one or more embodiments, a question may berepresented by a feature vector. From a given question, various questionattributes are extracted, which question attributes may belong to one ofmultiple types of information that is associated with any newly askedquestion, such information types include question meta data, questioncontent and user data. In the following discussion, the term featurefamily may be used to denote one attribute, e.g., category, parentcategory, hour, etc., extracted from the data. The attribute may benumerical, categorical or set-valued (e.g. the set of word tokens of thetitle). Where learning is performed using a gradient-based method, forexample, categorical attributes may be converted to binary features, andnumeric attributes may be binned.

FIG. 7 provides a table with examples of feature families that may beextracted in accordance with one or more embodiments. The “sparsity”column in the table describes the fraction of nonzero features of eachfeature-family out of all possible values of that family in allquestions; and the “feat.-type” column depicts a manner in which thefeatures are represented, e.g., binary or numeric. For example, thecategory attribute is transformed into a binary feature vector with1,287 entries, whereas the title category-words attribute is transformedinto a numeric feature vector with 11,286 entries. In the example shownin FIG. 7, a final vector representation of a question may be formed bya concatenation of the feature vectors that correspond to the featurefamilies in the table shown in FIG. 7, resulting in a sparse48,028-dimensional feature vector.

Content features may be extracted from the questions title and body. Byway of a non-limiting example, features extracted from a titleassociated with a question may be separated from features extracted froma body of the question. A reasoning may be based on the possibility thatthe title and body may be used for different purposes, e.g., the titlemay be used to lure answerers to view the whole question while the bodyprovides detailed information. For example, the title is usually veryshort whereas the body is much longer. Hence, differentiating betweentitle and body features may allow for a better learning of theirindividual contributions and an improved control over the trainingprocedure.

In accordance with one or more embodiments, title category-tokens aretextual tokens, which may include the question's title, e.g. such tokensmay comprise tokens {did, I, eat, too, much, today} in connection withthe question did I eat too much today?. Tokens may have differentmeanings, as well as different impacts on expected answers, whenoccurring in different categories. For example, Jaguar may have adifferent meaning when used in connection with a Car Makes than whenused in connection with a Zoology category. Thus, a question categoryand or parent category may be used to provide improved worddisambiguation, and the question category may be associated with eachextracted token, e.g. Car Makes: Jaguar and Zoology: Jaguar.

While dimensionality of all tokens in all categories may amount to alarge number of tokens, many of the words that may be used as potentialtokens may not be useful. Hence, a feature may be selected by measuringan error reduction of each token, t, in a category c, based on a squaredloss:

Δ L(c, t) = L(c) − L(c, t)${L(c)} = {\sum\limits_{i \in {Q{(c)}}}\left( {\mu_{c} - y_{i}} \right)^{2}}$${L\left( {c,t} \right)} = {{\frac{{Q\left( {c,t} \right)}}{{Q(c)}}{\sum\limits_{i \in {Q{({c,t})}}}\left( {\mu_{c,t} - y_{i}} \right)^{2}}} + {\frac{{Q\left( {c,{⫬ t}} \right)}}{{Q(c)}}{\sum\limits_{i \in {Q{({c,{⫬ \; t}})}}}\left( {\mu_{c,{⫬ \; t}} - y_{i}} \right)^{2}}}}$

In the above example, Q(c) is the set of questions in category c, Q(c,t) is the set of questions in c that contain token t, Q(c,

t) is the set of questions in c that do not contain t, y_(i) is thenumber of answers of question i,

$\frac{1}{{Q(c)}}{\sum\limits_{i \in {Q{(c)}}}y_{i}}$is the average over target values in category, c, μ_(c,t), is theaverage for all questions containing t in c, and c, μ_(c,)

_(t) is the average for questions not containing t in c. This featureselection heuristic may be geared towards reduction of mean squarederror (MSE) by taking into account the size of categories. Thus, betweentwo word-category pairs with similar prediction power, the higherranking pair refers to a larger category.

By way of one non-limiting example, the <c, t> tokens may be sorted byΔL(c,t) and the top 11,286 category-token pairs may be selected. FIG. 8provides an example of a table showing a list of fifteen top selected<c, t> pairs ordered by respective ΔL(c,t) values.

Similarly and with respect to body category-tokens, category-tokens maybe extracted from a question's body.

In accordance with one or more embodiment, a mean sentiment of thequestion may be calculated by sentiment analysis tool. The strength ofthe positive sentiment and the strength of the negative sentiment of thetitle may be extracted separately. Sentiment strengths may be scaledsuch that 0 corresponds to neutral and 4 corresponds to an extremelystrong sentiment. A sentence can be associated with both positive andnegative strength, e.g., I like dogs but I really hate when they startbarking might be associated with a positive strength 1 (slightlypositive) and a negative strength 4 (strongly negative). In a similarmanner, an average of the positive and negative sentiments of allsentences in a question's body may be determined.

A supervised Latent Dirichlet Allocation (LDA) model may be trained onthe text of questions, combining titles and bodies. Supervised LDA iscapable of finding topics that are strongly indicative of some responsevariable. In accordance with one or more embodiments, a supervised LDAmodel may be generated using a small subset of the training set, wherethe number of answers serves as a target variable.

Some of the topics obtained are shown FIG. 9, together with the learnedweight of the supervised regression component of each topic. FIG. 9provides some non-limiting examples of supervised LDA topics,represented by their ten most prominent word tokens, together with theirsupervised model weights. Positive (negative) weights are associatedwith topics likely to get more (less) answers than average. The trainedsupervised LDA model may be used to estimate the number of answers foreach question (either in the training or actual set) and the estimatemay be added as a feature.

A Title WH feature family may be used to capture a question type, suchas “where” versus “how to” questions, as indicated by the “WH” wordsthat appear in the title of the question: what, when, where, which, who,why, how, is, do and was. The latter three are typically meant tocapture yes/no questions. The “Body WH” feature family may be used toextract “WH” words from the question's body. Title length is the lengthof the title measured by number of tokens after stopword removal. Sincethe title may be limited, e.g., limited in length to 110 characters, thenumber of possible values may be constrained.

The length of the body of a question may be measured by number of tokensafter stopword removal. This feature may be binned on an exponentialscale. Title URL may specify the number of URLs that appear within thequestion's title. Similar to a title URL feature, a body URL feature mayprovide a count of the number of URLs in the question's body. By way ofa non-limiting example, following a link requires extra labor from theanswerer, potentially affecting answering behavior.

The feature set may include asker features, which may comprise a set offeatures capturing information related to the user posting a question.An asker identity feature may be used to capture the influence of theidentity of the asker on the mean number of answers that the asker'squestions might receive. A user identity feature may include a number ofsub-features, such as fields of interest of the user, a writing stylethat the user uses, communities with which the user interacts, anickname and avatar of the user, etc.

A mean number of answers to past questions may also be included; thepast mean number of answers an asker received may provide a goodindicator for a number of answers a future question posed by the usermight receive.

By way of a non-limiting example, a mean, or average, number of answersfor questions in a training set may be extracted for each user/asker.For users with significant history, e.g., users that ask a large numberof questions, this feature may provide a robust estimate for theexpected number of answers for a future question; however, for thoseusers who have asked a very few questions, which may be a large numberof users, this feature might not be as effective. To remedy thisproblem, for each user, the number of training set questions may becounted and the count may be used as an indicator of the robustness ofthe mean number of answers feature.

More specifically, let c_(u) be the number of training questions of auser u. User u may be assigned a value that is determined to beb_(u)=1+┌2 log₂ c_(u)┐. Test or validation users with no trainingquestion are assigned a bin number b_(u)=0. The values of b_(u) may betruncated at 25. This feature family may be associated with a featurevector, e.g., a vector that is 25 in length, and for each asker, theb_(u) entry of the feature vector stores the training set mean number ofanswers of the user. For new test or validation users, the value iszero. This allows the learning algorithm to tune a weight of thisfeature to the number of training questions of the user, e.g., train thevalue to the confidence in the empirical value of the mean.

A number of training set questions may be a feature family that capturesinformation complimentary to the number of answers feature family, andutilizes information of the total number of questions, in the trainingset, that were asked by a given user. A value of b_(u) may be used.

In accordance with one or more embodiments, feature families may useinformation extracted from non-textual parts of a question unrelated tothe user asking the question, such as category, parent category, hour,day of the week, week of the year, etc. A category feature may be usedto capture a strong prior of mean number of answers in the category.Each category may be viewed as a community of askers and answerers,where each community has unique characteristics and different dynamics.Categories may differ in volume of activity, number of active users,ratio between regular users and one-time visitors as well as in mean andstandard deviation of the number of answers. The distributions ofquestions among categories and among users may be extremely skewed, witha long tale of sparsely populated categories and users with a singlequestion. Where categories are organized in a taxonomy, or hierarchicalstructure, the structure may be used to extract a parent category givena question category. The parent category of the assigned category may beextracted as an additional feature family. This feature family may beless sparse than the category feature family and may be helpful forquestions that are assigned to rare categories.

An hour feature identifies the hour that the question was posted, andmay be used to capture potential daily answering patterns. For example,higher answering rates during the day should affect the average numberof answers per question. A day of week feature identifies the day ofweek the question was posted. Similar to the hour feature, the day ofthe week feature captures weekly patterns, e.g., increased traffic onweekends. A week of year feature identifies the week the question wasposted. The week of the year feature family captures user activity thatmay be affected by yearly patterns, such as holidays, school vacations,seasons etc.

In accordance with one or more embodiments, a regression model may beused as model 220. Of course it should be apparent that any modelingapproach may be used. By way of a non-limiting example, a training setof questions may be denoted as (X, Y)={x_(i), y_(i)}, where x_(i) is afeature vector representation of question q_(i) and y_(i)ϵ{0, 1, 2, . .. } is a number of answers. A prediction target may be denoted byZ=log₂(Y+1). A single feature family may be denoted by X^(α), and aconcatenation of the features may be denoted by X.

A linear function b+w^(T)X=b+Σ_(α)w_(α) ^(T)X^(α) may be used to modeldependency of Z on X. A regularized squared loss may be used to optimizethe model parameters b and w. The following provides a non-limitingexample:

${L_{\tau}\left( {b,w} \right)} = {{\frac{1}{2}{\sum\limits_{i}\left( {z_{i} - \left( {b + {\sum\limits_{\alpha}{w_{\alpha}^{T}x_{i}^{\alpha}}}} \right)} \right)^{2}}} + {\frac{\lambda_{i}}{2}{\sum\limits_{\alpha}{w_{\alpha}}^{2}}} + {\frac{\lambda_{2}}{2}{\sum\limits_{{\langle{i,j}\rangle} \in A}{{w_{\beta\; i} - w_{\beta\; j}}}^{2}}}}$

Two regularization terms may be used to reduce the complexity of themodel: a standard ridge regression term, and a term that applies to allfeature families that represent a numerical attribute but are binned fortechnical reasons, such as week of the year and body length, forexample. A set of the corresponding neighboring feature pairs <i, j>,e.g. first and second week, or first and 24^(th) hour, is represented asA. To learn parameter values, a Stochastic Gradient Descent (SGD) may beused. Random permutations of the observations may be cycled through toachieve convergence. For each learning rate a schedule may be used,e.g., a schedule of the form

${\eta_{\alpha}^{t} = \frac{\eta_{\alpha}^{O}}{t + \tau}},$where t, the number of SGD updates and τ>0 are common to all featurefamilies and η_(α) ^(O) is specific to feature family. There may also bea specific learning rate to learn b. The parameters η, η_(α), λ₁, and λ₂may be tuned. In accordance with one or more embodiments, η, λ₁, and λ₂may be set to 0.01, 0 and 0.01, respectively.

FIG. 10 illustrates some components that can be used in connection withone or more embodiments of the present disclosure. In accordance withone or more embodiments of the present disclosure, one or more computingdevices, e.g., one or more servers, user devices or other computingdevice, are configured to comprise functionality described herein. Forexample, a computing device 1002 and/or 1004 can be configured toexecute program code, instructions, etc. to provide functionality inaccordance with one or more embodiments of the present disclosure.

Computing device 1002 can serve content to user computing devices 1004using a browser application via a network 1006. Data store 1008 can beused to store logs 210 and data stored in data store 212, and/or programcode, instructions, etc. to configure a server computer 1002 or othercomputing device to provide functionality in accordance with one or moreembodiments of the present disclosure, etc.

The user computing device 1004 can be any computing device, includingwithout limitation a personal computer, personal digital assistant(PDA), wireless device, cell phone, internet appliance, media player,home theater system, and media center, or the like.

For the purposes of this disclosure a computing device includes aprocessor and memory for storing and executing program code, data andsoftware, and may be provided with an operating system that allows theexecution of software applications in order to manipulate data. Acomputing device such as server 1002 and the user computing device 1004can include one or more processors, memory, a removable media reader,network interface, display and interface, and one or more input devices,e.g., keyboard, keypad, mouse, etc. and input device interface, forexample. One skilled in the art will recognize that server 1002 and usercomputing device 1004 may be configured in many different ways andimplemented using many different combinations of hardware, software, orfirmware.

In accordance with one or more embodiments, a computing device 1002 canmake a user interface available to a user computing device 1004 via thenetwork 1006. The user interface made available to the user computingdevice 1004 can include content items, or identifiers (e.g., URLs)selected for the user interface in accordance with one or moreembodiments of the present invention. In accordance with one or moreembodiments, computing device 1002 makes a user interface available to auser computing device 1004 by communicating a definition of the userinterface to the user computing device 1004 via the network 1006. Theuser interface definition can be specified using any of a number oflanguages, including without limitation a markup language such asHypertext Markup Language, scripts, applets and the like. The userinterface definition can be processed by an application executing on theuser computing device 1004, such as a browser application, to output theuser interface on a display coupled, e.g., a display directly orindirectly connected, to the user computing device 1004. In accordancewith one or more embodiments, the user interface may be used to displayfeedback comprising one or more prediction measures of the likelihoodthat user members of the community will respond to the request forinput. In accordance with one or more such embodiments, the feedback maybe based on a prediction, e.g., prediction 226, generated by computingdevice 1002 or 1004 using model definition 220.

In an embodiment the network 1006 may be the Internet, an intranet (aprivate version of the Internet), or any other type of network. Anintranet is a computer network allowing data transfer between computingdevices on the network. Such a network may comprise personal computers,mainframes, servers, network-enabled hard drives, and any othercomputing device capable of connecting to other computing devices via anintranet. An intranet uses the same Internet protocol suit as theInternet. Two of the most important elements in the suit are thetransmission control protocol (TCP) and the Internet protocol (IP).

As discussed, a network may couple devices so that communications may beexchanged, such as between a server computing device and a clientcomputing device or other types of devices, including between wirelessdevices coupled via a wireless network, for example. A network may alsoinclude mass storage, such as network attached storage (NAS), a storagearea network (SAN), or other forms of computer or machine readablemedia, for example. A network may include the Internet, one or morelocal area networks (LANs), one or more wide area networks (WANs),wire-line type connections, wireless type connections, or anycombination thereof. Likewise, sub-networks, such as may employdiffering architectures or may be compliant or compatible with differingprotocols, may interoperate within a larger network. Various types ofdevices may, for example, be made available to provide an interoperablecapability for differing architectures or protocols. As one illustrativeexample, a router may provide a link between otherwise separate andindependent LANs. A communication link or channel may include, forexample, analog telephone lines, such as a twisted wire pair, a coaxialcable, full or fractional digital lines including T1, T2, T3, or T4 typelines, Integrated Services Digital Networks (ISDNs), Digital SubscriberLines (DSLs), wireless links including satellite links, or othercommunication links or channels, such as may be known to those skilledin the art. Furthermore, a computing device or other related electronicdevices may be remotely coupled to a network, such as via a telephoneline or link, for example.

A wireless network may couple client devices with a network. A wirelessnetwork may employ stand-alone ad-hoc networks, mesh networks, WirelessLAN (WLAN) networks, cellular networks, or the like. A wireless networkmay further include a system of terminals, gateways, routers, or thelike coupled by wireless radio links, or the like, which may movefreely, randomly or organize themselves arbitrarily, such that networktopology may change, at times even rapidly. A wireless network mayfurther employ a plurality of network access technologies, includingLong Term Evolution (LTE), WLAN, Wireless Router (WR) mesh, or 2nd, 3rd,or 4th generation (2G, 3G, or 4G) cellular technology, or the like.Network access technologies may enable wide area coverage for devices,such as client devices with varying degrees of mobility, for example.For example, a network may enable RF or wireless type communication viaone or more network access technologies, such as Global System forMobile communication (GSM), Universal Mobile Telecommunications System(UMTS), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced,Wideband Code Division Multiple Access (WCDMA), Bluetooth, 802.11b/g/n,or the like. A wireless network may include virtually any type ofwireless communication mechanism by which signals may be communicatedbetween devices, such as a client device or a computing device, betweenor within a network, or the like.

Signal packets communicated via a network, such as a network ofparticipating digital communication networks, may be compatible with orcompliant with one or more protocols. Signaling formats or protocolsemployed may include, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX,Appletalk, or the like. Versions of the Internet Protocol (IP) mayinclude IPv4 or IPv6. The Internet refers to a decentralized globalnetwork of networks. The Internet includes local area networks (LANs),wide area networks (WANs), wireless networks, or long haul publicnetworks that, for example, allow signal packets to be communicatedbetween LANs. Signal packets may be communicated between nodes of anetwork, such as, for example, to one or more sites employing a localnetwork address. A signal packet may, for example, be communicated overthe Internet from a user site via an access node coupled to theInternet. Likewise, a signal packet may be forwarded via network nodesto a target site coupled to the network via a network access node, forexample. A signal packet communicated via the Internet may, for example,be routed via a path of gateways, servers, etc. that may route thesignal packet in accordance with a target address and availability of anetwork path to the target address.

It should be apparent that embodiments of the present disclosure can beimplemented in a client-server environment such as that shown in FIG. 8.Alternatively, embodiments of the present disclosure can be implementedwith other environments. As one non-limiting example, a peer-to-peer (orP2P) network may employ computing power or bandwidth of networkparticipants in contrast with a network that may employ dedicateddevices, such as dedicated servers, for example; however, some networksmay employ both as well as other approaches. A P2P network may typicallybe used for coupling nodes via an ad hoc arrangement or configuration. Apeer-to-peer network may employ some nodes capable of operating as botha “client” and a “server.”

FIG. 11 is a detailed block diagram illustrating an internalarchitecture of a computing device, e.g., a computing device such asserver 1002 or user computing device 1004, in accordance with one ormore embodiments of the present disclosure. As shown in FIG. 11,internal architecture 1100 includes one or more processing units,processors, or processing cores, (also referred to herein as CPUs) 1112,which interface with at least one computer bus 1102. Also interfacingwith computer bus 1102 are computer-readable medium, or media, 1106,network interface 1114, memory 1104, e.g., random access memory (RAM),run-time transient memory, read only memory (ROM), etc., media diskdrive interface 1120 as an interface for a drive that can read and/orwrite to media including removable media such as floppy, CD-ROM, DVD,etc. media, display interface 1110 as interface for a monitor or otherdisplay device, keyboard interface 1116 as interface for a keyboard,pointing device interface 1118 as an interface for a mouse or otherpointing device, and miscellaneous other interfaces not shownindividually, such as parallel and serial port interfaces, a universalserial bus (USB) interface, and the like.

Memory 1104 interfaces with computer bus 1102 so as to provideinformation stored in memory 1104 to CPU 1112 during execution ofsoftware programs such as an operating system, application programs,device drivers, and software modules that comprise program code, and/orcomputer-executable process steps, incorporating functionality describedherein, e.g., one or more of process flows described herein. CPU 1112first loads computer-executable process steps from storage, e.g., memory1104, computer-readable storage medium/media 1106, removable mediadrive, and/or other storage device. CPU 1112 can then execute the storedprocess steps in order to execute the loaded computer-executable processsteps. Stored data, e.g., data stored by a storage device, can beaccessed by CPU 1112 during the execution of computer-executable processsteps.

Persistent storage, e.g., medium/media 1106, can be used to store anoperating system and one or more application programs. Persistentstorage can also be used to store device drivers, such as one or more ofa digital camera driver, monitor driver, printer driver, scanner driver,or other device drivers, web pages, content files, playlists and otherfiles. Persistent storage can further include program modules and datafiles used to implement one or more embodiments of the presentdisclosure, e.g., listing selection module(s), targeting informationcollection module(s), and listing notification module(s), thefunctionality and use of which in the implementation of the presentdisclosure are discussed in detail herein.

For the purposes of this disclosure a computer readable medium storescomputer data, which data can include computer program code that isexecutable by a computer, in machine readable form. By way of example,and not limitation, a computer readable medium may comprise computerreadable storage media, for tangible or fixed storage of data, orcommunication media for transient interpretation of code-containingsignals. Computer readable storage media, as used herein, refers tophysical or tangible storage (as opposed to signals) and includeswithout limitation volatile and non-volatile, removable andnon-removable media implemented in any method or technology for thetangible storage of information such as computer-readable instructions,data structures, program modules or other data. Computer readablestorage media includes, but is not limited to, RAM, ROM, EPROM, EEPROM,flash memory or other solid state memory technology, CD-ROM, DVD, orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other physical ormaterial medium which can be used to tangibly store the desiredinformation or data or instructions and which can be accessed by acomputer or processor.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client or server or both. In this regard, anynumber of the features of the different embodiments described herein maybe combined into single or multiple embodiments, and alternateembodiments having fewer than, or more than, all of the featuresdescribed herein are possible. Functionality may also be, in whole or inpart, distributed among multiple components, in manners now known or tobecome known. Thus, myriad software/hardware/firmware combinations arepossible in achieving the functions, features, interfaces andpreferences described herein. Moreover, the scope of the presentdisclosure covers conventionally known manners for carrying out thedescribed features and functions and interfaces, as well as thosevariations and modifications that may be made to the hardware orsoftware or firmware components described herein as would be understoodby those skilled in the art now and hereafter.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client or server or both. In this regard, anynumber of the features of the different embodiments described herein maybe combined into single or multiple embodiments, and alternateembodiments having fewer than, or more than, all of the featuresdescribed herein are possible. Functionality may also be, in whole or inpart, distributed among multiple components, in manners now known or tobecome known. Thus, myriad software/hardware/firmware combinations arepossible in achieving the functions, features, interfaces andpreferences described herein. Moreover, the scope of the presentdisclosure covers conventionally known manners for carrying out thedescribed features and functions and interfaces, as well as thosevariations and modifications that may be made to the hardware orsoftware or firmware components described herein as would be understoodby those skilled in the art now and hereafter.

While the system and method have been described in terms of one or moreembodiments, it is to be understood that the disclosure need not belimited to the disclosed embodiments. It is intended to cover variousmodifications and similar arrangements included within the spirit andscope of the claims, the scope of which should be accorded the broadestinterpretation so as to encompass all such modifications and similarstructures. The present disclosure includes any and all embodiments ofthe following claims.

The invention claimed is:
 1. A method comprising: receiving, via acommunity information sharing system server and a user interface, inputfrom a user member of a community of users of a community-drivenapplication used for online access to the community information sharingsystem, the input comprising at least a portion of a request, of theuser for an answer, to be directed to the community of users;determining, via the community information sharing system server andusing a prediction model, a probability of the user receiving an answer,from the community of users, to the request, probability determinationbeing performed in response to receipt of the input comprising at leasta portion of the request for an answer to be directed to the communityof users, the prediction model using the input, received from the userand comprising at least a portion of the request for an answer, indetermining the probability of the user receiving an answer; andproviding, via the community information sharing system server and fordisplay in the user interface, the determined probability of the userreceiving an answer from the community of users to the request of theuser for an answer, provision of the determined probability causing thedetermined probability to be displayed in the user interface as feedbackto the user in response to the user's input received prior to receivingposting input to post the request from the user, the feedback furthercomprising a notification identifying at least a portion of the requestdetermined to have a potential to reduce the probability of response bythe community of users.
 2. The method of claim 1, wherein the feedbackfurther comprises an estimated number of answers by the community ofusers.
 3. The method of claim 1, wherein the request for a responsecomprises a question, each response by a member of the user communitycomprises an answer to the question, and the determined probability is aprobability that the community of users will respond with at least oneanswer to the question.
 4. The method of claim 3, wherein the feedbackfurther comprises an estimated number of answers by the community ofusers.
 5. The method of claim 1, the providing the feedback to the useris responsive to at least a portion of the request entered by the user,and the feedback is provided prior to the user submitting the requestfor access by the community of users.
 6. The method of claim 1, theprediction model is generated using training data comprising informationcollected about past questions and corresponding answers and usersproviding the past questions and the corresponding answers.
 7. Themethod of claim 1, the notification comprising highlighting the at leasta portion of the request determined to have a potential to reduce theprobability of response by the community of users.
 8. The method ofclaim 1, the probability determination is made prior to receivingposting input to post the request from the user.
 9. A communityinformation sharing system server comprising: a processor and anon-transitory computer-readable storage medium for tangibly storingthereon program logic for execution by the processor, the stored programlogic comprising: receiving logic executed by the processor forreceiving input from a user member of a community of users of acommunity-driven application used for online access to the communityinformation sharing system, the input comprising at least a portion of arequest, of the user for an answer, to be directed to the community ofusers; determining logic executed by the processor for determining,using a prediction model, a probability of the user receiving an answer,from the community of users, to the request, probability determinationbeing performed in response to receipt of the input comprising at leasta portion of the request for an answer to be directed to the communityof users, the prediction model using the input, received from the userand comprising at least a portion of the request for an answer, indetermining the probability of the user receiving an answer; andproviding logic executed by the one or more processors for providing,for display in the user interface, the determined probability of theuser receiving an answer from the community of users to the request ofthe user for an answer, provision of the determined probability causingthe determined probability to be displayed in the user interface asfeedback to the user in response to the user's input received prior toreceiving posting input to post the request from the user, the feedbackfurther comprising a notification identifying at least a portion of therequest determined to have a potential to reduce the probability ofresponse by the community of users.
 10. The server of claim 9, whereinthe feedback further comprises an estimated number of responses by thecommunity of users.
 11. The server of claim 9, wherein the request for aresponse comprises a question, each response by a member of the usercommunity comprises an answer to the question, and the determinedprobability is a probability that the community of users will respondwith at least one answer to the question.
 12. The server of claim 11,wherein the feedback further comprises an estimated number of answers bythe community of users.
 13. The server of claim 9, the providing thefeedback to the user is responsive to at least a portion of the requestentered by the user, and the feedback is provided prior to the usersubmitting the request for access by the community of users.
 14. Theserver of claim 9, the prediction model is generated using training datacomprising information collected about past questions and correspondinganswers and users providing the past questions and the correspondinganswers.
 15. The server of claim 9, the notification comprisinghighlighting the at least a portion of the request determined to have apotential to reduce the probability of response by the community ofusers.
 16. A computer readable non-transitory storage medium fortangibly storing thereon computer readable instructions, that whenexecuted by a community information sharing system server, perform afunction comprising: receiving input from a user member of a communityof users of a community-driven application used for online access to thecommunity information sharing system, the input comprising at least aportion of a request, of the user for an answer, to be directed to thecommunity of users; determining, using a prediction model, a probabilityof the user receiving an answer, from the community of users to therequest, probability determination being performed in response toreceipt of the input comprising at least a portion of the request for ananswer to be directed to the community of users, the prediction modelusing the input, received from the user and comprising at least aportion of the request for an answer, in determining the probability ofthe user receiving an answer; and providing, for display in the userinterface, the determined probability of the user receiving an answerfrom the community of users to the request of the user for an answer,provision of the determined probability causing the determinedprobability to be displayed in the user interface as feedback to theuser in response to the user's input received prior to receiving postinginput to post the request from the user, the feedback further comprisinga notification identifying at least a portion of the request determinedto have a potential to reduce the probability of response by thecommunity of users.
 17. The medium of claim 16, wherein the feedbackfurther comprises an estimated number of answers by the community ofusers.
 18. The medium of claim 16, wherein the request for a responsecomprises a question, each response by a member of the user communitycomprises an answer to the question, and the determined probability is aprobability that the community of users will respond with at least oneanswer to the question.
 19. The medium of claim 18, wherein the feedbackfurther comprises an estimated number of answers by the community ofusers.
 20. The medium of claim 16, the providing the feedback to theuser is responsive to at least a portion of the request entered by theuser, and the feedback is provided prior to the user submitting therequest for access by the community of users.
 21. The medium of claim16, the prediction model is generated using training data comprisinginformation collected about past questions and corresponding answers andusers providing the past questions and the corresponding answers. 22.The medium of claim 16, the notification comprising highlighting the atleast a portion of the request determined to have a potential to reducethe probability of response by the community of users.